Campaign 2016 (PhD Chapter 1)

This series of files compile all analyses done during Chapter 1 for the regional campaign of 2016:

All analyses have been done with PRIMER-e 6 and R 3.6.0.

Click on the table of contents in the left margin to assess a specific analysis.
Click on a figure to zoom it

To assess maps and figures, click here.
To go back to the summary page, click here.

Caracteristics of each campaign

2014 2016 2017
Sampling date August-September June to August July
Criteria for perturbation Potentially impacted if close to the city or industries, References outside the bay Human-impacted if in a region with a highly populated area, industries and maritime activities, Reference if none of these criteria Human-impacted if in a region with a highly populated area, industries and maritime activities, Reference if none of these criteria
Regions considered BSI BSI, CPC, BDA, MR BSI, MR
Number of sampled stations 40 (20 HI, 20 R) 78 (26 BSI, 19 CPC, 18 BDA, 15 MR) 126 (111 BSI, 15 MR)
Parameters sampled Organic matter yes yes yes
Photosynthetic pigments no yes yes
Sediment grain-size yes yes yes
Heavy-metals yes yes (for a limited number of stations) no (interpolated based on 2014 and 2016 values)
Benthic communities Compartment targeted Macro-infauna Macro-infauna Macro-infauna
Sieved used 500 µm 1 mm 500 µm and 1 mm
Conservation technique Formaldehyle Formaldehyle Formaldehyle
Others N.A. N.A. N.A.

We used data from subtidal ecosystems (see metadata files for more information). Only stations that have been sampled both for abiotic parameters and benthic species were included.

Selected variables for the analyses:

Abundances of Mesodesma arctatum (Marc) and Cistenides granulata (Cgra) were also considered (see IndVal and SIMPER results).

As data is missing for metal concentrations outside BSI, two Designs have been used:

Statistics for each variable considered:

BSI, CPC, BDA, MR (without heavy metals)
  Mean SD SE Median Min Max 95% CI
depth 20.362 16.975 1.947 15.500 1.000 65.900 3.816
om 0.691 0.738 0.085 0.384 0.168 3.863 0.166
gravel 0.046 0.118 0.014 0.000 0.000 0.809 0.027
sand 0.707 0.352 0.040 0.883 0.000 1.001 0.079
silt 0.203 0.288 0.033 0.031 0.000 0.942 0.065
clay 0.044 0.099 0.011 0.006 0.000 0.497 0.022
S 4.671 2.895 0.332 4.000 0.000 13.000 0.651
N 19.408 25.758 2.955 12.000 0.000 142.000 5.791
H 1.059 0.603 0.069 1.025 0.000 2.307 0.136
J 0.721 0.295 0.034 0.838 0.000 1.000 0.066
BSI (heavy metals)
  Mean SD SE Median Min Max 95% CI
arsenic 4.300 4.759 0.971 2.500 0.800 21.300 1.904
cadmium 0.153 0.048 0.010 0.145 0.080 0.270 0.019
chromium 49.667 24.539 5.009 40.500 17.000 111.000 9.817
copper 9.787 7.764 1.585 7.800 2.400 28.800 3.106
iron 45356.633 17343.813 3540.291 39733.450 21938.100 85408.600 6938.843
manganese 776.663 362.884 74.073 677.850 318.400 1657.100 145.181
mercury 0.023 0.017 0.004 0.019 0.007 0.091 0.007
lead 5.421 3.021 0.617 4.650 1.900 12.200 1.209
zinc 59.358 30.340 6.193 47.700 26.900 141.400 12.138

1. Data manipulation

For the following analyses, independant variables are habitat parameters and heavy metal concentrations, dependant variables are diversity indices.

1.1. Identification of outliers

To identify stations that are not consistent with the others, we used the multivariate Cook’s Distance (CD) on the uncorrelated variables. A significative threshold of 4 times the mean of CD has been established.

Design 1

Based on Cook’s Distance, we identified stations 60, 72, 80 and 96 as general outliers. They have been deleted for the following analyses of Design 1.

Design 2

Based on Cook’s Distance, we identified stations 108 and 110 as general outliers. They have been deleted for the following analyses of Design 2.

1.2. Correlations between parameters

Correlations have been calculated with Spearman’s rank coefficient.

Design 1

Correlation coefficients between habitat parameters (Design 1)
  om gravel sand silt clay
om 1 -0.068 -0.807 0.714 0.706
gravel -0.068 1 -0.192 -0.37 -0.329
sand -0.807 -0.192 1 -0.772 -0.768
silt 0.714 -0.37 -0.772 1 0.973
clay 0.706 -0.329 -0.768 0.973 1

According to these results, the following variables are highly correlated (\(|\rho|\) > 0.80) so they have been considered together in the regressions of Design 1:

  • silt and clay (clay deleted)

We decided to keep sand, even if it is correlated with om, to stay consistant with the 2014 campaign.

Design 2

Correlation coefficients between heavy metals concentrations (Design 2)
  arsenic cadmium chromium copper iron manganese mercury lead zinc
arsenic 1 0.492 0.736 0.876 0.773 0.399 0.646 0.816 0.903
cadmium 0.492 1 0.757 0.41 0.766 0.881 0.154 0.708 0.663
chromium 0.736 0.757 1 0.712 0.825 0.767 0.463 0.85 0.879
copper 0.876 0.41 0.712 1 0.633 0.38 0.572 0.829 0.89
iron 0.773 0.766 0.825 0.633 1 0.755 0.429 0.745 0.842
manganese 0.399 0.881 0.767 0.38 0.755 1 0.105 0.584 0.628
mercury 0.646 0.154 0.463 0.572 0.429 0.105 1 0.627 0.545
lead 0.816 0.708 0.85 0.829 0.745 0.584 0.627 1 0.898
zinc 0.903 0.663 0.879 0.89 0.842 0.628 0.545 0.898 1

According to these results, the following variables are highly correlated (\(|\rho|\) > 0.80) so they have been considered together in the regressions of Design 2:

  • cadmium and manganese (manganese deleted)
  • copper, lead and zinc (copper and zinc deleted)

We decided to keep arsenic, even though it is correlated with the copper/lead/zinc group, to stay consistant with the 2014 campaign.

2. Permutational Analyses of Covariance

Results of univariate PermANCOVAs on parameters and multivariate PermANCOVA on the whole benthic community with depth as covariate are presented in the table below. Variables were normalized and abundances were (log+1) transformed.

Variable Condition Region(Co) Depth Significative groups of similar regions (p > 0.05)
om S S {CPC BDA MR}
gravel All regions in the same group
sand S All regions in the same group
silt S S {BSI CPC BDA}, {BDA MR}
clay {BSI BDA MR}, {CPC MR}
S (1 mm) S {BSI CPC MR}, {CPC BDA MR}
N (1 mm) All regions in the same group
H (1 mm) s~ S {CPC BDA MR}, {BSI MR}
J (1 mm) {BSI CPC MR}, {CPC BDA MR}
ALL SPECIES (1 mm) S S

3. Similarity and characteristic species

Let’s have a look at the \(\beta\) diversity within our conditions and sites.

Results of the PERMDISP routine are shown below (mean and SE of the deviation from centroid for each group, i.e. multivariate dispersion). Abundances were (log+1) transformed.

Condition or Site Mean SE
HI 64.6 0.83
R 61.9 1.14
BSI 62.9 1.18
CPC 60.2 2.25
BDA 61.1 1.93
MR 58.2 2.12

No significative relationships were found for either factor, but BSI was detected different of CPC and MR.

Here are the values of the mean Bray-Curtis dissimilarity for each group.

Mean within-group dissimilarity for each condition or region (Bray-Curtis, %)
  HI R BSI CPC BDA MR
Mean BC 0.917 0.878 0.903 0.87 0.882 0.835

The following analyses allowed to detect species as characteristic of each condition. We used results from PRIMER to justify further their choice.

##                       cluster indicator_value probability
## cistenides_granulata        1          0.2836       0.018
## macoma_calcarea             1          0.2326       0.002
## ennucula_tenuis             1          0.1860       0.018
## eudorellopsis_integra       1          0.1395       0.029
## mesodesma_arctatum          2          0.2342       0.007
## harmothoe_imbricata         2          0.1975       0.010
## glycera_alba                2          0.1212       0.039
## psammonyx_nobilis           2          0.1212       0.029
## 
## Sum of probabilities                 =  50.871 
## 
## Sum of Indicator Values              =  5.89 
## 
## Sum of Significant Indicator Values  =  1.52 
## 
## Number of Significant Indicators     =  8 
## 
## Significant Indicator Distribution
## 
## 1 2 
## 4 4
SIMPER results (mean between-group dissimilarity: 0.926 )
  average sd ratio ava avb cumsum
echinarachnius_parma 0.0984 0.136 0.721 0.689 0.42 0.106
mesodesma_arctatum 0.07 0.129 0.542 0.605 0.0995 0.182
cistenides_granulata 0.0609 0.0948 0.643 0.176 0.565 0.248
strongylocentrotus_sp 0.0427 0.0758 0.563 0.27 0.249 0.294
nephtys_caeca 0.0425 0.0556 0.764 0.359 0.23 0.34
limecola_balthica 0.0313 0.0578 0.542 0.234 0.18 0.373
scoloplos_armiger 0.0295 0.065 0.453 0.14 0.256 0.405
macoma_calcarea 0.0274 0.0569 0.482 0 0.312 0.435
harmothoe_imbricata 0.0257 0.0583 0.44 0.217 0.0161 0.462
amphipholis_squamata 0.0238 0.0611 0.389 0.042 0.241 0.488
protomedeia_grandimana 0.0228 0.0538 0.424 0.183 0.169 0.513
psammonyx_nobilis 0.0189 0.0592 0.32 0.185 0 0.533
thyasira_sp 0.0186 0.0469 0.397 0.021 0.241 0.553
ennucula_tenuis 0.0185 0.0422 0.438 0 0.241 0.573
mya_arenaria 0.0174 0.034 0.513 0.063 0.168 0.592
ciliatocardium_ciliatum 0.014 0.045 0.312 0.0908 0.0766 0.607
goniada_maculata 0.0139 0.0354 0.391 0.021 0.173 0.622
glycera_dibranchiata 0.0134 0.043 0.31 0.021 0.0806 0.637
glycera_alba 0.0128 0.0408 0.313 0.172 0 0.65
ameritella_agilis 0.0117 0.0491 0.238 0 0.131 0.663
astarte_undata 0.0117 0.0388 0.301 0.142 0 0.676
astarte_subaequilatera 0.0106 0.0363 0.293 0.134 0 0.687
nucula_proxima 0.00992 0.0349 0.284 0 0.112 0.698
pygospio_elegans 0.00989 0.0449 0.22 0.137 0.0161 0.708
ophelia_limacina 0.00977 0.0299 0.327 0.042 0.0578 0.719
diastylis_sculpta 0.00966 0.0405 0.238 0.0488 0.0322 0.729
eudorellopsis_integra 0.00955 0.0267 0.358 0 0.153 0.74
ampharetidae_spp 0.00948 0.0277 0.342 0.0753 0.0535 0.75
yoldia_myalis 0.00913 0.0285 0.321 0.0543 0.0484 0.76
nephtys_bucera 0.00905 0.0256 0.354 0.063 0.0322 0.77
ampeliscidae_spp 0.00898 0.0253 0.354 0.063 0.0511 0.779
pontoporeia_femorata 0.00877 0.0404 0.217 0 0.132 0.789
bipalponephtys_neotena 0.00836 0.037 0.226 0 0.106 0.798
maldanidae_spp 0.00825 0.0272 0.303 0.0908 0.0322 0.807
pagurus_pubescens 0.00766 0.0231 0.331 0.0753 0.0161 0.815
polynoidae_spp 0.00756 0.0217 0.349 0.021 0.0952 0.823
ampharete_oculata 0.00725 0.0439 0.165 0.0666 0 0.831
phyllodoce_mucosa 0.00643 0.0241 0.267 0 0.106 0.838
phyllodocidae_spp 0.00629 0.0211 0.298 0.021 0.0484 0.845
phoxocephalus_holbolli 0.00621 0.0329 0.189 0 0.0827 0.851
testudinalia_testudinalis 0.00576 0.026 0.222 0.08 0 0.858
harpinia_propinqua 0.00547 0.0253 0.216 0.0753 0.0161 0.864
quasimelita_formosa 0.00486 0.0192 0.253 0 0.0739 0.869
nephtys_ciliata 0.00455 0.0213 0.214 0 0.0645 0.874
platyhelminthes 0.00429 0.0164 0.262 0 0.0484 0.878
lacuna_vincta 0.00427 0.0233 0.184 0 0.0417 0.883
cancer_irroratus 0.00405 0.0143 0.283 0.042 0.0161 0.887
nephtys_incisa 0.00399 0.0185 0.216 0.021 0.0161 0.892
arrhoges_occidentalis 0.00398 0.0167 0.239 0.0543 0 0.896

4. Univariate regressions

We used linear models for the all regressions on diversity indices. Outliers and correlated variables were removed from these analyses.

4.1. Simple regressions

These analyses have been do to explore the relationships between variables. As it is a huge number of results to interpret, only multiple regressions will be included in the article (see below).

Design 1

Adjusted R-squared of simple regressions for Design 1
  om gravel sand silt
S 0.09824 0.06215 0.0708 0.1258
N 0.01242 0.01491 0.03477 0.03467
H 0.09519 0.03329 0.06053 0.1134
J 0.004809 -0.0122 0.01178 0.01984
p-values of simple regressions for Design 1
  om gravel sand silt
S 0.00425 0.01962 0.01359 0.001309
N 0.1732 0.1542 0.06343 0.06371
H 0.004839 0.06765 0.02101 0.002229
J 0.2504 0.7054 0.1785 0.123

Design 2

Adjusted R-squared of simple regressions for Design 2
  arsenic cadmium chromium iron mercury lead
S -0.01268 -0.04896 -0.03331 -0.04823 -0.047 0.06622
N 0.008407 -0.04909 -0.03615 -0.04682 -0.04877 0.03425
H -0.01205 -0.03027 -0.001362 -0.02749 -0.02325 0.102
J -0.04952 -0.01768 -0.0304 -0.03285 -0.03656 -0.04851
p-values of simple regressions for Design 2
  arsenic cadmium chromium iron mercury lead
S 0.4008 0.8897 0.5762 0.8559 0.8132 0.1303
N 0.2907 0.8964 0.6107 0.8078 0.8796 0.2014
H 0.3967 0.543 0.3361 0.5155 0.478 0.08065
J 0.9251 0.4348 0.5443 0.5708 0.6162 0.8677

Furthermore, depth has been shown important for several parameters in the ANCOVAs, so here are the corresponding scatterplots.

4.2. Multiple regressions

This section presents analyses done (i) to determine which model (Design 1, Design 2) decribes the best the parameters and (ii) which variables are the most important to explain the parameters.

4.2.1. Best model selection

This step was not used here as both models were needed.

4.2.2. Significative variables selection

We identified which variables were selected after an AIC procedure to predict the best the parameters. Results of the variable selection, according to AIC, are shown on the tables below:

  • for the model of Design 1
Variable (or combination) S N H J
om
gravel - +
sand + - +
silt/clay + - + +
Adjusted \(R^{2}\) 0.17 0.1 0.18 0.02
  • for the model of Design 2
Variable (or combination) S N H J
arsenic
cadmium/manganese
chromium - - -
iron
mercury
lead/copper/zinc + + +
Adjusted \(R^{2}\) 0.29 0.16 0.21 0

Details of the regressions, with diagnostics and cross-validation, are summarized below.

Design 1

Species richness
## FULL MODEL
## Adjusted R2 is: 0.15
Fitting linear model: S ~ om + gravel + sand + silt
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -5.815 7.696 -0.7556 0.4526
om -0.1275 0.8173 -0.1561 0.8765
gravel 3.618 8.918 0.4057 0.6863
sand 10.11 7.78 1.3 0.1982
silt 15.05 9.997 1.505 0.137
Variance Inflation Factors
  om gravel sand silt
VIF 2.01 2.35 8.23 9.4 
## REDUCED MODEL
## Adjusted R2 is: 0.17
Fitting linear model: S ~ sand + silt
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.998 3.167 -0.9466 0.3471
sand 7.299 3.315 2.202 0.03102 *
silt 11.48 3.727 3.081 0.002963 * *
Variance Inflation Factors
  sand silt
VIF 3.55 3.55 
## RMSE for the full model: 2.60029 
## RMSE for the reduced model: 2.515746

Total abundance
## FULL MODEL
## Adjusted R2 is: 0.1
Fitting linear model: N ~ om + gravel + sand + silt
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 215.1 74.6 2.883 0.005295 * *
om 8.721 7.923 1.101 0.2749
gravel -244.9 86.46 -2.833 0.006085 * *
sand -199.8 75.42 -2.649 0.01006 *
silt -231.5 96.92 -2.389 0.01974 *
Variance Inflation Factors
  om gravel sand silt
VIF 2.01 2.35 8.23 9.4 
## REDUCED MODEL
## Adjusted R2 is: 0.1
Fitting linear model: N ~ gravel + sand + silt
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 176.7 66.07 2.674 0.009366 * *
gravel -201.2 76.9 -2.616 0.01094 *
sand -159.3 65.94 -2.416 0.0184 *
silt -166 76.63 -2.166 0.03379 *
Variance Inflation Factors
  gravel sand silt
VIF 2.09 7.18 7.42 
## RMSE for the full model: 30.52945 
## RMSE for the reduced model: 29.91383

Shannon index
## FULL MODEL
## Adjusted R2 is: 0.17
Fitting linear model: H ~ om + gravel + sand + silt
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.954 1.61 -1.834 0.07105
om -0.1016 0.171 -0.5938 0.5546
gravel 3.041 1.866 1.63 0.1079
sand 3.949 1.628 2.426 0.01798 *
silt 5.424 2.092 2.593 0.01168 *
Variance Inflation Factors
  om gravel sand silt
VIF 2.01 2.35 8.23 9.4 
## REDUCED MODEL
## Adjusted R2 is: 0.18
Fitting linear model: H ~ gravel + sand + silt
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.507 1.417 -1.769 0.08133
gravel 2.532 1.649 1.535 0.1295
sand 3.477 1.414 2.459 0.01649 *
silt 4.662 1.644 2.836 0.006011 * *
Variance Inflation Factors
  gravel sand silt
VIF 2.09 7.18 7.42 
## RMSE for the full model: 0.5255413 
## RMSE for the reduced model: 0.5269319

Piélou’s evenness
## FULL MODEL
## Adjusted R2 is: 0
Fitting linear model: J ~ om + gravel + sand + silt
  Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.3581 0.8682 -0.4124 0.6814
om -0.07058 0.0922 -0.7655 0.4467
gravel 1.11 1.006 1.104 0.2737
sand 1.07 0.8778 1.219 0.2273
silt 1.569 1.128 1.391 0.1688
Variance Inflation Factors
  om gravel sand silt
VIF 2.01 2.35 8.23 9.4 
## REDUCED MODEL
## Adjusted R2 is: 0.02
Fitting linear model: J ~ silt
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.6944 0.0404 17.19 8e-27 * * *
silt 0.1853 0.1187 1.561 0.123
Variance Inflation Factors
  silt
VIF 1 
## RMSE for the full model: 0.3016641 
## RMSE for the reduced model: 0.2855537

Design 2

Species richness
## FULL MODEL
## Adjusted R2 is: 0.23
Fitting linear model: S ~ arsenic + cadmium + chromium + iron + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.292 2.311 3.587 0.002696 * *
arsenic -0.06374 0.2517 -0.2532 0.8035
cadmium -4.257 22.55 -0.1888 0.8528
chromium -0.1487 0.1001 -1.486 0.1581
iron -8.05e-05 0.0001006 -0.8002 0.4361
mercury -52.39 38.5 -1.361 0.1937
lead 2.059 0.6635 3.103 0.007277 * *
Variance Inflation Factors
  arsenic cadmium chromium iron mercury lead
VIF 2.19 1.86 3.63 2.85 1.21 3.25 
## REDUCED MODEL
## Adjusted R2 is: 0.29
Fitting linear model: S ~ chromium + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.618 1.478 4.479 0.0002574 * * *
chromium -0.1919 0.07173 -2.675 0.01499 *
lead 1.677 0.532 3.153 0.005237 * *
Variance Inflation Factors
  chromium lead
VIF 2.7 2.7 
## RMSE for the full model: 3.108151 
## RMSE for the reduced model: 2.508174

Total abundance
## FULL MODEL
## Adjusted R2 is: 0.04
Fitting linear model: N ~ arsenic + cadmium + chromium + iron + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 34.8 15.49 2.247 0.04011 *
arsenic 0.7435 1.686 0.4409 0.6656
cadmium 19.5 151.1 0.129 0.8991
chromium -0.7334 0.6704 -1.094 0.2912
iron -0.0005478 0.000674 -0.8128 0.429
mercury -236.2 258 -0.9155 0.3744
lead 8.931 4.446 2.009 0.06288
Variance Inflation Factors
  arsenic cadmium chromium iron mercury lead
VIF 2.19 1.86 3.63 2.85 1.21 3.25 
## REDUCED MODEL
## Adjusted R2 is: 0.16
Fitting linear model: N ~ chromium + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 24.22 9.576 2.529 0.02046 *
chromium -0.9287 0.4648 -1.998 0.06024
lead 8.207 3.447 2.381 0.0279 *
Variance Inflation Factors
  chromium lead
VIF 2.7 2.7 
## RMSE for the full model: 24.0044 
## RMSE for the reduced model: 16.71375

Shannon index
## FULL MODEL
## Adjusted R2 is: 0.06
Fitting linear model: H ~ arsenic + cadmium + chromium + iron + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.35 0.431 3.133 0.006841 * *
arsenic -0.02749 0.04693 -0.5857 0.5668
cadmium -0.5051 4.206 -0.1201 0.906
chromium -0.02101 0.01866 -1.126 0.2778
iron -4.814e-06 1.876e-05 -0.2566 0.801
mercury -4.576 7.18 -0.6373 0.5335
lead 0.2943 0.1237 2.379 0.03107 *
Variance Inflation Factors
  arsenic cadmium chromium iron mercury lead
VIF 2.19 1.86 3.63 2.85 1.21 3.25 
## REDUCED MODEL
## Adjusted R2 is: 0.21
Fitting linear model: H ~ chromium + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.296 0.2619 4.949 8.918e-05 * * *
chromium -0.02438 0.01271 -1.918 0.07024
lead 0.2364 0.09427 2.508 0.02137 *
Variance Inflation Factors
  chromium lead
VIF 2.7 2.7 
## RMSE for the full model: 0.454785 
## RMSE for the reduced model: 0.4430522

Piélou’s evenness
## FULL MODEL
## Adjusted R2 is: -0.23
Fitting linear model: J ~ arsenic + cadmium + chromium + iron + mercury + lead
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.5733 0.2064 2.778 0.01407 *
arsenic -0.006316 0.02247 -0.2811 0.7825
cadmium 0.4514 2.013 0.2242 0.8256
chromium 0.006524 0.008932 0.7304 0.4764
iron 2.023e-06 8.981e-06 0.2252 0.8248
mercury 2.501 3.437 0.7275 0.4781
lead -0.05506 0.05924 -0.9295 0.3674
Variance Inflation Factors
  arsenic cadmium chromium iron mercury lead
VIF 2.19 1.86 3.63 2.85 1.21 3.25 
## REDUCED MODEL
## Adjusted R2 is: 0
Fitting linear model: J ~ 1
  Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.7955 0.04487 17.73 4.118e-14 * * *

Quitting from lines 444-448 (C1_analyses_16B.Rmd) Error in Qr$qr[p1, p1, drop = FALSE] : indice hors limites De plus : There were 31 warnings (use warnings() to see them)

## RMSE for the full model: 0.2719167 
## RMSE for the reduced model: 0.2163708

5. Multivariate regressions

Independant variables are habitat parameters or heavy metal concentrations, dependant variables are species abundances. Outliers and correlated variables have been excluded from the analysis.

This analysis has been done on PRIMER, with a DistLM to identify the variables that explain the most the community variability and with a dbRDA to plot the results.

Design 1

Design 2

Elliot Dreujou

2020-01-20